Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Agnes Begue 1, * Valentine Lebourgeois 1 Eric Bappel 1 Pierre Todoroff 2 Anne Pellegrino 1 Florence Baillarin 3 Bernard Siegmund 2
* Corresponding author
1 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
2 UPR 102 SCA - Systèmes de Cultures Annuelles
3 Spot Image - Spot Image
Abstract : Sugarcane is a semi-perennial grass whose cultivation is characterized by an extended harvest season lasting several months leading to very high spatio-temporal variability of the crop development and radiometry. The objective of this paper is to understand this variability in order to propose appropriate spectral indicators for yield forecast. To do this, we used ground observations and SPOT4 and SPOT5 time series acquired monthly over a 2-year period over Reunion Island and Guadeloupe (French West Indies). We showed that variations in the NDVI (Normalized Difference Vegetation Index) of sugarcane at the field scale are the result of the interaction between the sugarcane crop calendar and plant phenology in a given climatic environment. We linked these variations to crop variables measured in the field (LAI and leaf color), and derived simple, appropriate NDVI-based indicators of sugarcane yield components at the field scale (cane yield and sugar content). For biomass forecast, the best correlation (r² = 0.78) was obtained with images acquired about 2 months before the harvest season, when all the fields are fully developed but before the maturation stage. For sugar content, a polynomial relationship (r² = 0.75) was observed between the field NDVI acquired during the maturation stage and sugar content in the stalk.
Keywords : spatial variability time series NDVI Sugarcane Spatio-temporal Crop management Yield Production LAI sucrose
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Agnes Begue, Valentine Lebourgeois, Eric Bappel, Pierre Todoroff, Anne Pellegrino, et al.. Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI. International Journal of Remote Sensing, Taylor & Francis, 2010, 31 (20), pp.5391-5407. ⟨10.1080/01431160903349057⟩. ⟨cirad-00664047⟩

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